SoroushVahidi/scidocs huggingface dataset:
Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering
(2017)

Why-question answering (why-QA) is a task to retrieve answers (or answer passages) to why-questions (e.g., "why are tsunamis generated?") from a text archive. Several previously proposed methods for why-QA improved their performance by automatically recognizing causalities that are expressed with such explicit cues as "because" in answer passages and using the recognized causalities as a clue for finding proper answers. However, in answer passages, causalities might be implicitly expressed, (i.e., without any explicit cues): "An earthquake suddenly displaced sea water and a tsunami was generated." The previous works did not deal with such implicitly expressed causalities and failed to find proper answers that included the causalities. We improve why-QA based on the following two ideas. First, implicitly expressed causalities in one text might be expressed in other texts with explicit cues. If we can automatically recognize such explicitly expressed causalities from a text archive and use them to complement the implicitly expressed causalities in an answer passage, we can improve why-QA. Second, the causes of similar events tend to be described with a similar set of words (e.g., "seismic energy" and "tectonic plates" for "the Great East Japan Earthquake" and "the 1906 San Francisco Earthquake"). As such, even if we cannot find in a text archive any explicitly expressed cause of an event (e.g., "the Great East Japan Earthquake") expressed in a question (e.g., "Why did the Great East Japan earthquake happen?"), we might be able to identify its implicitly expressed causes with a set of words (e.g., "tectonic plates") that appear in the explicitly expressed cause of a similar event (e.g., "the 1906 San Francisco Earthquake").
 We implemented these two ideas in our multi-column convolutional neural networks with a novel attention mechanism, which we call causality attention. Through experiments on Japanese why-QA, we confirmed that our proposed method outperformed the state-of-the-art systems.
Analysis and design of compact two-way Wilkinson power dividers using coupled lines
(2009)

This paper analyzes the two-way Wilkinson power dividers using coupled lines as λ/4 impedance transformer to have more compact layout. The analysis shows that the input port matching is only influenced by the even mode impedance of the coupled lines. Once the even mode impedance is fixed, all other specifications of the power divider are determined by the odd mode impedance of the coupled lines. Design tradeoffs among the output matching, isolation and the odd mode impedance are shown. By using smaller odd mode impedance, layout constraint can be relaxed. To validate the analysis, Wilkinson power dividers using coupled lines with areas of 1.5 cm2 and 1.25 cm2 are designed together with a conventional divider with an area of 3.8 cm2. The experimental results of the three designs are comparable. When coupled lines are used, the layout is more compact with a reduction in size of more than 50% compared to the conventional design.
Organizational Chart Inference
(2015)

Nowadays, to facilitate the communication and cooperation among employees, a new family of online social networks has been adopted in many companies, which are called the "enterprise social networks" (ESNs). ESNs can provide employees with various professional services to help them deal with daily work issues. Meanwhile, employees in companies are usually organized into different hierarchies according to the relative ranks of their positions. The company internal management structure can be outlined with the organizational chart visually, which is normally confidential to the public out of the privacy and security concerns. In this paper, we want to study the IOC (Inference of Organizational Chart) problem to identify company internal organizational chart based on the heterogeneous online ESN launched in it. IOC is very challenging to address as, to guarantee smooth operations, the internal organizational charts of companies need to meet certain structural requirements (about its depth and width). To solve the IOC problem, a novel unsupervised method Create (ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1) social stratification of ESN users into different social classes, (2) supervision link inference from managers to subordinates, and (3) consecutive social classes matching to prune the redundant supervision links. Extensive experiments conducted on real-world online ESN dataset demonstrate that Create can perform very well in addressing the IOC problem.
The curvelet transform for image denoising
(2002)

We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction, stability against perturbations, ease of implementation, and low computational complexity. A central tool is Fourier-domain computation of an approximate digital Radon transform. We introduce a very simple interpolation in the Fourier space which takes Cartesian samples and yields samples on a rectopolar grid, which is a pseudo-polar sampling set based on a concentric squares geometry. Despite the crudeness of our interpolation, the visual performance is surprisingly good. Our ridgelet transform applies to the Radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain. Our curvelet transform uses our ridgelet transform as a component step, and implements curvelet subbands using a filter bank of a; trous wavelet filters. Our philosophy throughout is that transforms should be overcomplete, rather than critically sampled. We apply these digital transforms to the denoising of some standard images embedded in white noise. In the tests reported here, simple thresholding of the curvelet coefficients is very competitive with "state of the art" techniques based on wavelets, including thresholding of decimated or undecimated wavelet transforms and also including tree-based Bayesian posterior mean methods. Moreover, the curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems. The empirical results reported here are in encouraging agreement.
Embodied Construction Grammar
(2013)

Construction-based approaches to grammar are driven by the insight that meaning plays a crucial role in answering this question: the meanings of both words and grammatical structures can affect grammaticality, and they should therefore be incorporated into grammatical representations. To wit, the various approaches presented in this volume take this tack in predicting the grammaticality of utterances of all sorts, from the creative to the conventional. But focusing on utterance grammaticality removes utterances from their physical and social contexts of use. The result is an approach to language that resembles traditional anatomy, in which utterances are dissected, categorized, and explained in terms of their structural and distributional properties. Insights from such an approach can be both useful and informative in accounting for what utterances are or potentially could be. They do not, however, explain human language use—that is, the mechanisms and representations that human beings use to learn, produce, and understand utterances. If we are interested in these latter issues, issues more properly situated within cognitive science than analytical linguistics, then we find our investigations of grammar (and language more generally) guided by a different sort of question:
Discovering Diverse and Salient Threads in Document Collections
(2012)

We propose a novel probabilistic technique for modeling and extracting salient structure from large document collections. As in clustering and topic modeling, our goal is to provide an organizing perspective into otherwise overwhelming amounts of information. We are particularly interested in revealing and exploiting relationships between documents. To this end, we focus on extracting diverse sets of threads—singlylinked, coherent chains of important documents. To illustrate, we extract research threads from citation graphs and construct timelines from news articles. Our method is highly scalable, running on a corpus of over 30 million words in about four minutes, more than 75 times faster than a dynamic topic model. Finally, the results from our model more closely resemble human news summaries according to several metrics and are also preferred by human judges.
Current review of prepubertal vaginal bleeding.
(2017)

PURPOSE OF REVIEW
Prepubertal vaginal bleeding raises many concerns and evaluation and diagnosis may prove difficult for many providers. We aim to provide a comprehensive review and recent updates for those practitioners who care for these patients.


RECENT FINDINGS
Prompt management in the case of prepubertal vaginal bleeding is indicated, especially to rule out malignancy or abuse. If a child is reluctant to undergo examination, or if the extent of injury or source of bleeding cannot be determined, examination under anesthesia and vaginoscopy is recommended. Use of vaginoscopy allows for clear visualization of the vagina and cervix without distorting hymenal anatomy, as well as diagnosis and removal of a foreign body and evaluation of mucosal damage caused. In the case of sexual abuse, providers specifically trained in pediatrics need to be present, and safety of the patient should always be ensured.


SUMMARY
Careful history taking and targeted examination may lead to diagnosis in the case of prepubertal vaginal bleeding. However, in more difficult cases, practitioners should not hesitate to examine a patient in the operating room using general anesthesia to elicit the cause. Although sexual abuse and malignancy are always on the differential, most causes of bleeding are benign and easily treated.
Hypothesizing the Aptness of Social Media and the Information Richness Requirements of Disaster Management
(2012)

In this article, the author first analyzes the social presence theory, media richness theory and taskmedia fit to investigate the suitability of various types of Social Media in disaster management. Then, on the basis of this analysis, use of social media is proposed to facilitate the communication tasks involved in the interaction between disaster management agencies and communities during disaster management. Next the author adapt a conceptual framework that integrates three types of communication (involving disaster management agencies and communities). The framework is further used as a springboard to develop a number of hypotheses to predict the aptness of rich and lean types of Social Media against the media richness requirements of disaster management tasks.
Natural Image Bases to Represent Neuroimaging Data
(2013)

Visual inspection of neuroimagery is susceptible to human eye limitations. Computerized methods have been shown to be equally or more e↵ective than human clinicians in diagnosing dementia from neuroimages. Nevertheless, much of the work involves the use of domain expertise to extract hand–crafted features. The key technique in this paper is the use of cross–domain features to represent MRI data. We used a sparse autoencoder to learn a set of bases from natural images and then applied convolution to extract features from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Using this new representation, we classify MRI instances into three categories: Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Our approach, in spite of being very simple, achieved high classification performance, which is competitive with or better than other approaches.
Doing good or doing well ? Image motivation and monetary incentives in behaving prosocially
(2007)

Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially This paper experimentally examines image motivation – the desire to be liked and wellregarded by others – as a driver in prosocial behavior (doing good), and asks whether extrinsic monetary incentives (doing well) have a detrimental effect on prosocial behavior due to crowding out of image motivation. By definition, image depends on one’s behavior being visible to other people. Using this unique property we show that image is indeed an important part of the motivation to behave prosocially. Moreover, we show that extrinsic incentives interact with image motivation and are therefore less effective in public than in private. Together, these results imply that image motivation is crowded out by monetary incentives; which in turn means that monetary incentives are more likely to be counterproductive for public prosocial activities than for private ones. JEL Classification: D64, C90, H41
Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising
(2013)

Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula that characterizes the allowed undersampling of generalized sparse objects. The formula applies to approximate message passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar soft thresholding denoiser. This paper gives several examples including scalar denoisers not derived from convex penalization-the firm shrinkage nonlinearity and the minimax nonlinearity-and also nonscalar denoisers-block thresholding, monotone regression, and total variation minimization. Let the variables ε = k/N and δ = n/N denote the generalized sparsity and undersampling fractions for sampling the k-generalized-sparse N-vector x0 according to y=Ax0. Here, A is an n×N measurement matrix whose entries are iid standard Gaussian. The formula states that the phase transition curve δ = δ(ε) separating successful from unsuccessful reconstruction of x0 by AMP is given by δ = M(ε|Denoiser) where M(ε|Denoiser) denotes the per-coordinate minimax mean squared error (MSE) of the specified, optimally tuned denoiser in the directly observed problem y = x + z. In short, the phase transition of a noiseless undersampling problem is identical to the minimax MSE in a denoising problem. We prove that this formula follows from state evolution and present numerical results validating it in a wide range of settings. The above formula generates numerous new insights, both in the scalar and in the nonscalar cases.
Structural modeling and analysis of dengue-mediated inhibition of interferon signaling pathway.
(2015)

Dengue virus (DENV) belongs to the family Flaviviridae and can cause major health problems worldwide, including dengue fever and dengue shock syndrome. DENV replicon in human cells inhibits interferon α and β with the help of its non-structural proteins. Non-structural protein 5 (NS5) of DENV is responsible for the proteasome-mediated degradation of signal transducer and activator of transcription (STAT) 2 protein, which has been implicated in the development of resistance against interferon-mediated antiviral effect. This degradation of STAT2 primarily occurs with the help of E3 ubiquitin ligases. Seven in absentia homologue (SIAH) 2 is a host protein that can mediate the ubiquitination of proteins and is known for its interaction with NS5. In this study, comprehensive computational analysis was performed to characterize the protein-protein interactions between NS5, SIAH2, and STAT2 to gain insight into the residues and sites of interaction between these proteins. The objective of the study was to structurally characterize the NS5-STAT2, SIAH2-STAT2, and NS5-SIAH2 interactions along with the determination of the possible reaction pattern for the degradation of STAT2. Docking and physicochemical studies indicated that DENV NS5 may first interact with the host SIAH2, which can then proceed towards binding with STAT2 from the side of SIAH2. These implications are reported for the first time and require validation by wet-lab studies.
Visualizing cyber security: Usable workspaces
(2009)

The goal of cyber security visualization is to help analysts increase the safety and soundness of our digital infrastructures by providing effective tools and workspaces. Visualization researchers must make visual tools more usable and compelling than the text-based tools that currently dominate cyber analysts' tool chests. A cyber analytics work environment should enable multiple, simultaneous investigations and information foraging, as well as provide a solution space for organizing data. We describe our study of cyber-security professionals and visualizations in a large, high-resolution display work environment and the analytic tasks this environment can support. We articulate a set of design principles for usable cyber analytic workspaces that our studies have brought to light. Finally, we present prototypes designed to meet our guidelines and a usability evaluation of the environment.
Visual Analytics: Scope and Challenges
(2008)

In today’s applications data is produced at unprecedented rates. While the capacity to collect and store new data rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogenous, and dynamic volumes of information by integrating human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.
The base-rate fallacy and the difficulty of intrusion detection
(2000)

Many different demands can be made of intrusion detection systems. An important requirement is that an intrusion detection system be effective; that is, it should detect a substantial percentage of intrusions into the supervised system, while still keeping the false alarm rate at an acceptable level. This article demonstrates that, for a reasonable set of assumptions, the false alarm rate is the limiting factor for the performance of an intrusion detection system. This is due to the base-rate fallacy phenomenon, that in order to achieve substantial values of the Bayesian detection rate P(Intrusion***Alarm), we have to achieve a (perhaps in some cases unattainably) low false alarm rate. A selection of reports of intrusion detection performance are reviewed, and the conclusion is reached that there are indications that at least some types of intrusion detection have far to go before they can attain such low false alarm rates.
Generalized fisheye views
(1986)

In many contexts, humans often represent their own “neighborhood” in great detail, yet only major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.
Automatic Root Cause Analysis for LTE Networks Based on Unsupervised Techniques
(2016)

The increase in the size and complexity of current cellular networks is complicating their operation and maintenance tasks. While the end-to-end user experience in terms of throughput and latency has been significantly improved, cellular networks have also become more prone to failures. In this context, mobile operators start to concentrate their efforts on creating self-healing networks, i.e., those networks capable of troubleshooting in an automatic way, making the network more reliable and reducing costs. In this paper, an automatic diagnosis system based on unsupervised techniques for Long-Term Evolution (LTE) networks is proposed. In particular, this system is built through an iterative process, using self-organizing maps (SOMs) and Ward's hierarchical method, to guarantee the quality of the solution. Furthermore, to obtain a number of relevant clusters and label them properly from a technical point of view, an approach based on the analysis of the statistical behavior of each cluster is proposed. Moreover, with the aim of increasing the accuracy of the system, a novel adjustment process is presented. It intends to refine the diagnosis solution provided by the traditional SOM according to the so-called silhouette index and the most similar cause on the basis of the minimum Xth percentile of all distances. The effectiveness of the developed diagnosis system is validated using real and simulated LTE data by analyzing its performance and comparing it with reference mechanisms.
A journey to highly dynamic, self-adaptive service-based applications
(2008)

Future software systems will operate in a highly dynamic world. Systems will need to operate correctly despite of unespected changes in factors such as environmental conditions, user requirements, technology, legal regulations, and market opportunities. They will have to operate in a constantly evolving environment that includes people, content, electronic devices, and legacy systems. They will thus need the ability to continuously adapt themselves in an automated manner to react to those changes. To realize dynamic, self-adaptive systems, the service concept has emerged as a suitable abstraction mechanism. Together with the concept of the service-oriented architecture (SOA), this led to the development of technologies, standards, and methods to build service-based applications by flexibly aggregating individual services. This article discusses how those concepts came to be by taking two complementary viewpoints. On the one hand, it evaluates the progress in software technologies and methodologies that led to the service concept and SOA. On the other hand, it discusses how the evolution of the requirements, and in particular business goals, influenced the progress towards highly dynamic self-adaptive systems. Finally, based on a discussion of the current state of the art, this article points out the possible future evolution of the field.